Mobile Ghent

Mobile positioning data and transport: a theoretical, methodological and empirical discussion

24 October 2013

Bert van WeeDelft University of Technology

The Netherlands

Presentation: focus on travel behaviour

Theoretical options follow (mainly) from data. Therefore: data first

Not addressed, but very relevant:• Privacy restricted versus not.• Privacy, availability, legal aspects: probably dynamic. Role

of government very important• Open systems: more difficult to manage

Methods / data (partly linked to theory)

• Way more data – larger numbers, statistical significance• Cheaper• Better quality (though not always)• External quality checks• Use of ‘wisdom of the crowds’• Easier to collect• More options for (consistent) longitudinal data collection

Methods / data (continued)

• Solution of underreporting short trips• Solution for respondents getting tired of repeatedly

reporting• Rare events / difficult to select target groups. Start selecting

people at destination (as opposed to panel / selection via questions)

Methods (partly linked to theory)

• Combine ‘origin based’ (persons) with destination based (activity/destination)

• Why would people participate? Rewards. (Airmiles)

Theory:Why impacts theory: More data (numbers, data per person)

(non)response, disaggregation, impact behaviour

Not really fundamentally different. Nevertheless:

Theory:• Options to test new theoretical assumptions e.g. due to

larger numbers, more data per person• Options to discover new insights or formulate hypotheses

not based on a priori theory (Grounded Theory, data mining). A bit risky, but also new challenges

• Options to disaggregate further (e.g. mobility trends for specific groups of people)

Theory:• More locational detail: enrich related theories.• More longitudinal data: causalities.

Examples:• Testing theory of constant Travel Time Budgets: multiple

days, also short trips. Desaggregations.• Route choice under multiple conditions (e.g. weather)• Mode choice in case of changing mode choice (1 person)• Shopping behaviour (incl. fun shopping)

However

• Practice so far: The more (bigger) data, the less theoretical underpinnings, the less quality of analyses

• Data mining• Maybe lack of awareness quality data• Ignorance of self-selection effects (e.g. leave smartphone at

home for short trips; PT: smart phone users versus others)• Privacy (may even be linked to self-selection)

Empirical

• Adaptive and flexible event management• What do people do in case of emergency? Otherwise very

difficult to measure• Time space geography: action spaces: more and better data• Traffic flow (road, cars): many data, dynamics over time,

input for Satnav, short term forecasting:1. changes in speeds, flows2. if people would announce destination

• Walking, cycling (now often poor data)• Travel and activities during holiday• Better links between travel and activities:

not only ‘shopping’ but what kind of shopping (working, recreation)

• Recreational travel behaviour (some studies ignore recreational travel)

• Discover ‘bottlenecks’ / validate complaints of citizens

• ‘Objective’ data for prioritization of plans

Maybe police:• Speeding• Drivers of lorries: too long hours?

However:• Legal aspects• Privacy (big brother is watching you)

Other remarks

• We need to learn. • Risk of publication bias: only successful projects reported.

Network important!• This topic: one of many on Big (and partly open) Data.

Learn from lessons outside transport! Lot of literature in other areas (ICT), lot of grey literature

• Primary reflection: substitution for other data collection methods. Practice: generation (new ideas, new options). Future will show I overlooked key impacts on theory, data, empirical options.

• Reasons why we have mobile position based data has an impact on behaviour. E.g. train instead of car because of being online.